Massachusetts Institute of Technology's Institute for Medical Engineering & Science (IMES) has an immediate opening for a Postdoctoral Associate in Machine Learning for Medicine. We are seeking a highly-motivated individual to join a multidisciplinary research team to conduct cutting-edge machine learning research for health and medicine. The project offers opportunities to develop advanced machine learning methods to derive actionable insights from heterogeneous observational data from electronic health records (including clinical time series, medication/procedures, physician notes and reports, and physiological signals) for informed treatment decision making.
The ideal candidate will have demonstrated an outstanding capability for independent research and a solid publication record in top-tier machine learning, AI conferences, preferably in one or more of the following conferences: AAAI, ICML, NIPS, ICLR, KDD, IJCAI, AISTATS, UAI, MLHC or other top-tier ML conferences. Candidate must hold a Ph.D. degree in Computer Science, Machine Learning, Statistics, or a related field. Knowledge and experience in one or more of the following areas would be desirable: deep learning, interpretable models, representation learning, generative models, multivariate time series models, multitask/transfer learning, and reinforcement learning.
Duties will include conducting original research, publishing in top-tier machine learning conferences and scientific journals, mentoring students, and collaborating on research grant proposal writing.
Successful candidate will work with researchers and faculty members from MIT IMES and CSAIL. In addition to a curriculum vitae, applicants should submit a short statement of research interest and links to three most relevant publications to Li Lehman, lilehman<at>mit.edu
Li-wei Lehman, Ph.D. Research Scientist Laboratory for Computational Physiology Institute for Medical Engineering & Science Massachusetts Institute of Technology http://web.mit.edu/lilehman/www/